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dc.contributor.authorGawel, Danuta R.
dc.contributor.authorSerra-Musach, Jordi
dc.contributor.authorLilja, Sandra
dc.contributor.authorAagesen, Jesper
dc.contributor.authorArenas, Alex
dc.contributor.authorAsking, Bengt
dc.contributor.authorBengnér, Malin
dc.contributor.authorBjörkander, Janne
dc.contributor.authorBiggs, Sophie
dc.contributor.authorErnerudh, Jan
dc.contributor.authorHjortswang, Henrik
dc.contributor.authorKarlsson, Jan-Erik
dc.contributor.authorKöpsen, Mattias
dc.contributor.authorLee, Eun Jung
dc.contributor.authorLentini, Antonio
dc.contributor.authorLi, Xinxiu
dc.contributor.authorMagnusson, Mattias
dc.contributor.authorMartínez-Enguita, David
dc.contributor.authorMatussek, Andreas
dc.contributor.authorNestor, Colm E.
dc.contributor.authorSchäfer, Samuel
dc.contributor.authorSeifert, Oliver
dc.contributor.authorSonmez, Ceylan
dc.contributor.authorStjernman, Henrik
dc.contributor.authorTjärnberg, Andreas
dc.contributor.authorWu, Simon
dc.contributor.authorÅkesson, Karin
dc.contributor.authorShalek, Alexander K
dc.contributor.authorStenmarker, Margaretha
dc.contributor.authorZhang, Huan
dc.contributor.authorGustafsson, Mika
dc.contributor.authorBenson, Mikael
dc.date.accessioned2020-01-23T22:49:52Z
dc.date.available2020-01-23T22:49:52Z
dc.date.issued2019-07-30
dc.date.submitted2019-04-09
dc.identifier.issn1756-994X
dc.identifier.urihttps://hdl.handle.net/1721.1/123667
dc.description.abstractBackground: Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods: The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results: We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. Conclusions: Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease. Keywords: Network tools; scRNA-seq; Biomarker and drug discoveryen_US
dc.language.isoen
dc.publisherSpringer Science+Business Mediaen_US
dc.relation.isversionofhttps://doi.org/10.1186/s13073-019-0657-3en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceBioMed Central (BMC)en_US
dc.subjectGenetics(clinical)en_US
dc.subjectMolecular Medicineen_US
dc.subjectGeneticsen_US
dc.subjectMolecular Biologyen_US
dc.titleA validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseasesen_US
dc.typeArticleen_US
dc.identifier.citationGawel, Danuta R. et al. "A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases." Genome Medicine, 11, (July 2019): 47 © 2019 The Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemistryen_US
dc.contributor.departmentBroad Institute of MIT and Harvarden_US
dc.contributor.departmentBroad Institute of MIT and Harvarden_US
dc.contributor.departmentKoch Institute for Integrative Cancer Research at MITen_US
dc.relation.journalGenome Medicineen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-01-08T14:27:12Z
dspace.date.submission2020-01-08T14:27:14Z
mit.journal.volume11en_US
mit.journal.issue1en_US


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